// Copyright 2016 The Draco Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
#ifndef DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_MULTI_PARALLELOGRAM_ENCODER_H_
#define DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_MULTI_PARALLELOGRAM_ENCODER_H_

#include "draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_encoder.h"
#include "draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_parallelogram_shared.h"

namespace draco {

// Multi parallelogram prediction predicts attribute values using information
// from all opposite faces to the predicted vertex, compared to the standard
// prediction scheme, where only one opposite face is used (see
// prediction_scheme_parallelogram.h). This approach is generally slower than
// the standard parallelogram prediction, but it usually results in better
// prediction (5 - 20% based on the quantization level. Better gains can be
// achieved when more aggressive quantization is used).
template <typename DataTypeT, class TransformT, class MeshDataT>
class MeshPredictionSchemeMultiParallelogramEncoder
    : public MeshPredictionSchemeEncoder<DataTypeT, TransformT, MeshDataT> {
  public:
    using CorrType =
        typename PredictionSchemeEncoder<DataTypeT, TransformT>::CorrType;
    using CornerTable = typename MeshDataT::CornerTable;

    explicit MeshPredictionSchemeMultiParallelogramEncoder(
        const PointAttribute *attribute)
        : MeshPredictionSchemeEncoder<DataTypeT, TransformT, MeshDataT>(
              attribute) {}
    MeshPredictionSchemeMultiParallelogramEncoder(const PointAttribute *attribute,
            const TransformT &transform,
            const MeshDataT &mesh_data)
        : MeshPredictionSchemeEncoder<DataTypeT, TransformT, MeshDataT>(
              attribute, transform, mesh_data) {}

    bool ComputeCorrectionValues(
        const DataTypeT *in_data, CorrType *out_corr, int size,
        int num_components, const PointIndex *entry_to_point_id_map) override;
    PredictionSchemeMethod GetPredictionMethod() const override {
        return MESH_PREDICTION_MULTI_PARALLELOGRAM;
    }

    bool IsInitialized() const override {
        return this->mesh_data().IsInitialized();
    }
};

template <typename DataTypeT, class TransformT, class MeshDataT>
bool MeshPredictionSchemeMultiParallelogramEncoder<DataTypeT, TransformT,
     MeshDataT>::
     ComputeCorrectionValues(const DataTypeT *in_data, CorrType *out_corr,
                             int size, int num_components,
const PointIndex * /* entry_to_point_id_map */) {
    this->transform().Initialize(in_data, size, num_components);
    const CornerTable *const table = this->mesh_data().corner_table();
    const std::vector<int32_t> *const vertex_to_data_map =
        this->mesh_data().vertex_to_data_map();

    std::unique_ptr<DataTypeT[]> pred_vals(new DataTypeT[num_components]());
    std::unique_ptr<DataTypeT[]> parallelogram_pred_vals(
        new DataTypeT[num_components]());

    // We start processing from the end because this prediction uses data from
    // previous entries that could be overwritten when an entry is processed.
    for (int p = this->mesh_data().data_to_corner_map()->size() - 1; p > 0; --p) {
        const CornerIndex start_corner_id =
            this->mesh_data().data_to_corner_map()->at(p);

        // Go over all corners attached to the vertex and compute the predicted
        // value from the parallelograms defined by their opposite faces.
        CornerIndex corner_id(start_corner_id);
        int num_parallelograms = 0;
        for (int i = 0; i < num_components; ++i) {
            pred_vals[i] = static_cast<DataTypeT>(0);
        }
        while (corner_id != kInvalidCornerIndex) {
            if (ComputeParallelogramPrediction(
                        p, corner_id, table, *vertex_to_data_map, in_data, num_components,
                        parallelogram_pred_vals.get())) {
                for (int c = 0; c < num_components; ++c) {
                    pred_vals[c] += parallelogram_pred_vals[c];
                }
                ++num_parallelograms;
            }

            // Proceed to the next corner attached to the vertex.
            corner_id = table->SwingRight(corner_id);
            if (corner_id == start_corner_id) {
                corner_id = kInvalidCornerIndex;
            }
        }
        const int dst_offset = p * num_components;
        if (num_parallelograms == 0) {
            // No parallelogram was valid.
            // We use the last encoded point as a reference.
            const int src_offset = (p - 1) * num_components;
            this->transform().ComputeCorrection(
                in_data + dst_offset, in_data + src_offset, out_corr + dst_offset);
        } else {
            // Compute the correction from the predicted value.
            for (int c = 0; c < num_components; ++c) {
                pred_vals[c] /= num_parallelograms;
            }
            this->transform().ComputeCorrection(in_data + dst_offset, pred_vals.get(),
                                                out_corr + dst_offset);
        }
    }
    // First element is always fixed because it cannot be predicted.
    for (int i = 0; i < num_components; ++i) {
        pred_vals[i] = static_cast<DataTypeT>(0);
    }
    this->transform().ComputeCorrection(in_data, pred_vals.get(), out_corr);
    return true;
}

}  // namespace draco

#endif  // DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_MULTI_PARALLELOGRAM_ENCODER_H_
